Aviation sector – flying high on analytics

“Flying in the sky” is a dream that has always attracted the human race. In the last 100 years, the aviation sector has touched every part of the world. It has led to the amalgamation of cultures, thereby transforming the world into a global village. The aviation sector is a high cost sector (fixed costs - fleet size, airports and operating costs - fuel, taxes, landing charges). In other words, the industry has to constantly overcome the profitability issue. Increasing security issues and extreme weather have also increased the operational hurdles. It is also witnessing the emergence of low cost carriers and modification in consumer preference.

However, the aviation sector is at the forefront in capturing data across the spectrum - aviation design, engine performance among others. Commercial airlines maintain records of passengers, booking transactions, operational costs, cargo information, flight/sales/promotions data, flights routes, flights schedule. The efficient use of data along with analytics can assist in providing information on customer satisfaction, fuel prices and underutilized capacity.

The available historical data can be evaluated to forecast the future market, understand customer preferences, and identify security threats across the globe. Business Analytics can condense the gap between existing information and strategic impact for competitive advantage. Major airlines such as American and Southwest airlines have utilized analytics to transform the business operations. Currently, analytics is being used in airports, aircraft manufacturing among others.

Some of the key functions using analytics in the aviation sector:

Aircraft Maintenance

Commercial airlines spend a huge amount of money on maintenance functions to ensure aircrafts perform at optimal level. At present, most of the airlines leverage on-condition/preventive maintenance procedures based on the failure mode calculations which are done after parts testing under varying situations. The situations would differ depending on the external factors/human errors which could impact the life span of components, thereby reducing the operational efficiency of aircrafts. According to a study by Federal Aviation Administration (FAA), annually a jet engine generates data (20TB). Analytics can be used to forecast the failure of component by comparing and assessing data received from various sensors.

Supply Chain Management (OEM Perspective)

Commercial airlines stock parts for daily operations from OEM. At present, OEMs/vendors need 8 to 10 weeks to supply a part which is out of stock. Hence, sometimes airlines would look at other sources which would affect the profit margin of the OEM. Analytics would assist the OEMs/suppliers to predict the volume/quantity of parts an airline would order in future after evaluating data and future trends in the aviation sector. This would enable the OEM to revamp the production plan and ensure parts supply within a short time.

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